Overview

Dataset statistics

Number of variables20
Number of observations227
Missing cells110
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory35.6 KiB
Average record size in memory160.6 B

Variable types

Categorical2
Numeric18

Warnings

Country has a high cardinality: 227 distinct values High cardinality
Net migration has 3 (1.3%) missing values Missing
Infant mortality (per 1000 births) has 3 (1.3%) missing values Missing
Literacy (%) has 18 (7.9%) missing values Missing
Phones (per 1000) has 4 (1.8%) missing values Missing
Climate has 22 (9.7%) missing values Missing
Birthrate has 3 (1.3%) missing values Missing
Deathrate has 4 (1.8%) missing values Missing
Agriculture has 15 (6.6%) missing values Missing
Industry has 16 (7.0%) missing values Missing
Service has 15 (6.6%) missing values Missing
Country is uniformly distributed Uniform
Country has unique values Unique
Population has unique values Unique
Coastline (coast/area ratio) has 44 (19.4%) zeros Zeros
Net migration has 62 (27.3%) zeros Zeros
Arable (%) has 9 (4.0%) zeros Zeros
Crops (%) has 28 (12.3%) zeros Zeros

Reproduction

Analysis started2021-03-01 02:42:37.191611
Analysis finished2021-03-01 02:44:24.758821
Duration1 minute and 47.57 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
Mauritania
 
1
El Salvador
 
1
Bhutan
 
1
Austria
 
1
Poland
 
1
Other values (222)
222 

Length

Max length33
Median length9
Mean length9.925110132
Min length5

Characters and Unicode

Total characters2253
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st rowAfghanistan
2nd rowAlbania
3rd rowAlgeria
4th rowAmerican Samoa
5th rowAndorra
ValueCountFrequency (%)
Mauritania 1
 
0.4%
El Salvador 1
 
0.4%
Bhutan 1
 
0.4%
Austria 1
 
0.4%
Poland 1
 
0.4%
Argentina 1
 
0.4%
Georgia 1
 
0.4%
New Zealand 1
 
0.4%
Cuba 1
 
0.4%
India 1
 
0.4%
Other values (217)217
95.6%
2021-03-01T08:14:26.008703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7
 
2.3%
islands7
 
2.3%
the4
 
1.3%
saint4
 
1.3%
new3
 
1.0%
guinea3
 
1.0%
united3
 
1.0%
and2
 
0.6%
congo2
 
0.6%
st2
 
0.6%
Other values (262)272
88.0%

Most occurring characters

ValueCountFrequency (%)
309
13.7%
a294
13.0%
i167
 
7.4%
n158
 
7.0%
e146
 
6.5%
r115
 
5.1%
o101
 
4.5%
u79
 
3.5%
t77
 
3.4%
s75
 
3.3%
Other values (47)732
32.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1623
72.0%
Space Separator309
 
13.7%
Uppercase Letter297
 
13.2%
Other Punctuation23
 
1.0%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a294
18.1%
i167
10.3%
n158
9.7%
e146
9.0%
r115
 
7.1%
o101
 
6.2%
u79
 
4.9%
t77
 
4.7%
s75
 
4.6%
l75
 
4.6%
Other values (16)336
20.7%
ValueCountFrequency (%)
S34
 
11.4%
M25
 
8.4%
B23
 
7.7%
C22
 
7.4%
G21
 
7.1%
A19
 
6.4%
I19
 
6.4%
T17
 
5.7%
N15
 
5.1%
P13
 
4.4%
Other values (15)89
30.0%
ValueCountFrequency (%)
.8
34.8%
&7
30.4%
,7
30.4%
'1
 
4.3%
ValueCountFrequency (%)
309
100.0%
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1920
85.2%
Common333
 
14.8%

Most frequent character per script

ValueCountFrequency (%)
a294
15.3%
i167
 
8.7%
n158
 
8.2%
e146
 
7.6%
r115
 
6.0%
o101
 
5.3%
u79
 
4.1%
t77
 
4.0%
s75
 
3.9%
l75
 
3.9%
Other values (41)633
33.0%
ValueCountFrequency (%)
309
92.8%
.8
 
2.4%
&7
 
2.1%
,7
 
2.1%
'1
 
0.3%
-1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII2253
100.0%

Most frequent character per block

ValueCountFrequency (%)
309
13.7%
a294
13.0%
i167
 
7.4%
n158
 
7.0%
e146
 
6.5%
r115
 
5.1%
o101
 
4.5%
u79
 
3.5%
t77
 
3.4%
s75
 
3.3%
Other values (47)732
32.5%

Region
Categorical

Distinct11
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
SUB-SAHARAN AFRICA
51 
LATIN AMER. & CARIB
45 
ASIA (EX. NEAR EAST)
28 
WESTERN EUROPE
28 
OCEANIA
21 
Other values (6)
54 

Length

Max length35
Median length35
Mean length31.08810573
Min length20

Characters and Unicode

Total characters7057
Distinct characters25
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowASIA (EX. NEAR EAST)
2nd rowEASTERN EUROPE
3rd rowNORTHERN AFRICA
4th rowOCEANIA
5th rowWESTERN EUROPE
ValueCountFrequency (%)
SUB-SAHARAN AFRICA 51
22.5%
LATIN AMER. & CARIB 45
19.8%
ASIA (EX. NEAR EAST) 28
12.3%
WESTERN EUROPE 28
12.3%
OCEANIA 21
9.3%
NEAR EAST 16
 
7.0%
C.W. OF IND. STATES 12
 
5.3%
EASTERN EUROPE 12
 
5.3%
NORTHERN AFRICA 6
 
2.6%
NORTHERN AMERICA 5
 
2.2%
2021-03-01T08:14:26.524296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
africa57
 
9.5%
sub-saharan51
 
8.5%
latin45
 
7.5%
amer45
 
7.5%
45
 
7.5%
carib45
 
7.5%
east44
 
7.3%
near44
 
7.3%
europe40
 
6.7%
ex28
 
4.7%
Other values (11)156
26.0%

Most occurring characters

ValueCountFrequency (%)
3827
54.2%
A625
 
8.9%
E370
 
5.2%
R349
 
4.9%
S241
 
3.4%
N235
 
3.3%
I216
 
3.1%
T167
 
2.4%
C143
 
2.0%
.109
 
1.5%
Other values (15)775
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator3827
54.2%
Uppercase Letter2969
42.1%
Other Punctuation154
 
2.2%
Dash Punctuation51
 
0.7%
Open Punctuation28
 
0.4%
Close Punctuation28
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
A625
21.1%
E370
12.5%
R349
11.8%
S241
 
8.1%
N235
 
7.9%
I216
 
7.3%
T167
 
5.6%
C143
 
4.8%
B99
 
3.3%
U91
 
3.1%
Other values (9)433
14.6%
ValueCountFrequency (%)
.109
70.8%
&45
29.2%
ValueCountFrequency (%)
3827
100.0%
ValueCountFrequency (%)
(28
100.0%
ValueCountFrequency (%)
)28
100.0%
ValueCountFrequency (%)
-51
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4088
57.9%
Latin2969
42.1%

Most frequent character per script

ValueCountFrequency (%)
A625
21.1%
E370
12.5%
R349
11.8%
S241
 
8.1%
N235
 
7.9%
I216
 
7.3%
T167
 
5.6%
C143
 
4.8%
B99
 
3.3%
U91
 
3.1%
Other values (9)433
14.6%
ValueCountFrequency (%)
3827
93.6%
.109
 
2.7%
-51
 
1.2%
&45
 
1.1%
(28
 
0.7%
)28
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII7057
100.0%

Most frequent character per block

ValueCountFrequency (%)
3827
54.2%
A625
 
8.9%
E370
 
5.2%
R349
 
4.9%
S241
 
3.4%
N235
 
3.3%
I216
 
3.1%
T167
 
2.4%
C143
 
2.0%
.109
 
1.5%
Other values (15)775
 
11.0%

Population
Real number (ℝ≥0)

UNIQUE

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28740284.37
Minimum7026
Maximum1313973713
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:26.836770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7026
5-th percentile28324.9
Q1437624
median4786994
Q317497772.5
95-th percentile87948963.7
Maximum1313973713
Range1313966687
Interquartile range (IQR)17060148.5

Descriptive statistics

Standard deviation117891326.5
Coefficient of variation (CV)4.101954074
Kurtosis91.80568199
Mean28740284.37
Median Absolute Deviation (MAD)4695910
Skewness9.200223032
Sum6524044551
Variance1.389836487 × 1016
MonotocityNot monotonic
2021-03-01T08:14:27.227386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43033561
 
0.4%
139029721
 
0.4%
119871211
 
0.4%
488468231
 
0.4%
332412591
 
0.4%
399218331
 
0.4%
4527761
 
0.4%
385368691
 
0.4%
78629441
 
0.4%
55487021
 
0.4%
Other values (217)217
95.6%
ValueCountFrequency (%)
70261
0.4%
75021
0.4%
94391
0.4%
118101
0.4%
132871
0.4%
ValueCountFrequency (%)
13139737131
0.4%
10953519951
0.4%
2984442151
0.4%
2454527391
0.4%
1880782271
0.4%

Area (sq. mi.)
Real number (ℝ≥0)

Distinct226
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean598226.9559
Minimum2
Maximum17075200
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:27.555473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile155.1
Q14647.5
median86600
Q3441811
95-th percentile2291612.8
Maximum17075200
Range17075198
Interquartile range (IQR)437163.5

Descriptive statistics

Standard deviation1790282.244
Coefficient of variation (CV)2.992647232
Kurtosis41.76486359
Mean598226.9559
Median Absolute Deviation (MAD)86240
Skewness5.964245
Sum135797519
Variance3.205110512 × 1012
MonotocityNot monotonic
2021-03-01T08:14:27.852343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1022
 
0.9%
4311
 
0.4%
150071
 
0.4%
278301
 
0.4%
6931
 
0.4%
828801
 
0.4%
1319401
 
0.4%
652001
 
0.4%
3012301
 
0.4%
114371
 
0.4%
Other values (216)216
95.2%
ValueCountFrequency (%)
21
0.4%
71
0.4%
211
0.4%
261
0.4%
281
0.4%
ValueCountFrequency (%)
170752001
0.4%
99846701
0.4%
96314201
0.4%
95969601
0.4%
85119651
0.4%

Pop. Density (per sq. mi.)
Real number (ℝ≥0)

Distinct219
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean379.0471366
Minimum0
Maximum16271.5
Zeros1
Zeros (%)0.4%
Memory size1.9 KiB
2021-03-01T08:14:28.227298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.46
Q129.15
median78.8
Q3190.15
95-th percentile822.58
Maximum16271.5
Range16271.5
Interquartile range (IQR)161

Descriptive statistics

Standard deviation1660.185825
Coefficient of variation (CV)4.379892801
Kurtosis74.22464222
Mean379.0471366
Median Absolute Deviation (MAD)59.4
Skewness8.284885541
Sum86043.7
Variance2756216.972
MonotocityNot monotonic
2021-03-01T08:14:28.586649image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.82
 
0.9%
49.62
 
0.9%
97.72
 
0.9%
372.52
 
0.9%
66.62
 
0.9%
77.42
 
0.9%
69.82
 
0.9%
2.72
 
0.9%
337.41
 
0.4%
8.41
 
0.4%
Other values (209)209
92.1%
ValueCountFrequency (%)
01
0.4%
11
0.4%
1.81
0.4%
2.21
0.4%
2.51
0.4%
ValueCountFrequency (%)
16271.51
0.4%
161831
0.4%
6482.21
0.4%
6355.71
0.4%
3989.71
0.4%

Coastline (coast/area ratio)
Real number (ℝ≥0)

ZEROS

Distinct151
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.1653304
Minimum0
Maximum870.66
Zeros44
Zeros (%)19.4%
Memory size1.9 KiB
2021-03-01T08:14:28.930376image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.73
Q310.345
95-th percentile91.758
Maximum870.66
Range870.66
Interquartile range (IQR)10.245

Descriptive statistics

Standard deviation72.28686315
Coefficient of variation (CV)3.415343007
Kurtosis87.13588882
Mean21.1653304
Median Absolute Deviation (MAD)0.73
Skewness8.221680027
Sum4804.53
Variance5225.390584
MonotocityNot monotonic
2021-03-01T08:14:29.195986image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
044
 
19.4%
0.094
 
1.8%
0.134
 
1.8%
0.213
 
1.3%
0.243
 
1.3%
0.13
 
1.3%
0.233
 
1.3%
0.373
 
1.3%
0.153
 
1.3%
0.032
 
0.9%
Other values (141)155
68.3%
ValueCountFrequency (%)
044
19.4%
0.011
 
0.4%
0.032
 
0.9%
0.042
 
0.9%
0.051
 
0.4%
ValueCountFrequency (%)
870.661
0.4%
331.661
0.4%
310.691
0.4%
214.671
0.4%
2051
0.4%

Net migration
Real number (ℝ)

MISSING
ZEROS

Distinct157
Distinct (%)70.1%
Missing3
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean0.038125
Minimum-20.99
Maximum23.06
Zeros62
Zeros (%)27.3%
Memory size1.9 KiB
2021-03-01T08:14:29.492837image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-20.99
5-th percentile-7.5605
Q1-0.9275
median0
Q30.9975
95-th percentile7.6045
Maximum23.06
Range44.05
Interquartile range (IQR)1.925

Descriptive statistics

Standard deviation4.889269211
Coefficient of variation (CV)128.2431269
Kurtosis6.393233327
Mean0.038125
Median Absolute Deviation (MAD)0.985
Skewness0.1274019855
Sum8.54
Variance23.90495342
MonotocityNot monotonic
2021-03-01T08:14:29.774066image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
062
27.3%
-0.712
 
0.9%
4.052
 
0.9%
2.072
 
0.9%
-0.392
 
0.9%
-0.312
 
0.9%
-0.072
 
0.9%
5.371
 
0.4%
6.591
 
0.4%
-5.691
 
0.4%
Other values (147)147
64.8%
(Missing)3
 
1.3%
ValueCountFrequency (%)
-20.991
0.4%
-20.711
0.4%
-13.921
0.4%
-13.871
0.4%
-12.071
0.4%
ValueCountFrequency (%)
23.061
0.4%
18.751
0.4%
16.291
0.4%
14.181
0.4%
11.681
0.4%

Infant mortality (per 1000 births)
Real number (ℝ≥0)

MISSING

Distinct220
Distinct (%)98.2%
Missing3
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean35.50696429
Minimum2.29
Maximum191.19
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:30.102169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile4.2795
Q18.15
median21
Q355.705
95-th percentile102.888
Maximum191.19
Range188.9
Interquartile range (IQR)47.555

Descriptive statistics

Standard deviation35.38989877
Coefficient of variation (CV)0.9967030267
Kurtosis1.869631065
Mean35.50696429
Median Absolute Deviation (MAD)14.845
Skewness1.429943065
Sum7953.56
Variance1252.444935
MonotocityNot monotonic
2021-03-01T08:14:30.399025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.953
 
1.3%
12.622
 
0.9%
4.392
 
0.9%
7.721
 
0.4%
24.181
 
0.4%
73.451
 
0.4%
8.61
 
0.4%
121.691
 
0.4%
12.361
 
0.4%
85.221
 
0.4%
Other values (210)210
92.5%
(Missing)3
 
1.3%
ValueCountFrequency (%)
2.291
0.4%
2.771
0.4%
2.971
0.4%
3.261
0.4%
3.311
0.4%
ValueCountFrequency (%)
191.191
0.4%
163.071
0.4%
143.641
0.4%
130.791
0.4%
128.871
0.4%

GDP ($ per capita)
Real number (ℝ≥0)

Distinct130
Distinct (%)57.5%
Missing1
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean9689.823009
Minimum500
Maximum55100
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:30.711501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile700
Q11900
median5550
Q315700
95-th percentile29475
Maximum55100
Range54600
Interquartile range (IQR)13800

Descriptive statistics

Standard deviation10049.13851
Coefficient of variation (CV)1.037081741
Kurtosis1.553530991
Mean9689.823009
Median Absolute Deviation (MAD)4200
Skewness1.375923912
Sum2189900
Variance100985184.9
MonotocityNot monotonic
2021-03-01T08:14:30.977108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8007
 
3.1%
7006
 
2.6%
19005
 
2.2%
18005
 
2.2%
6004
 
1.8%
13004
 
1.8%
90004
 
1.8%
17004
 
1.8%
22004
 
1.8%
29004
 
1.8%
Other values (120)179
78.9%
ValueCountFrequency (%)
5003
1.3%
6004
1.8%
7006
2.6%
8007
3.1%
9002
 
0.9%
ValueCountFrequency (%)
551001
0.4%
378002
0.9%
360001
0.4%
350001
0.4%
346001
0.4%

Literacy (%)
Real number (ℝ≥0)

MISSING

Distinct140
Distinct (%)67.0%
Missing18
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean82.83827751
Minimum17.6
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:31.305213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum17.6
5-th percentile41.82
Q170.6
median92.5
Q398
95-th percentile99.8
Maximum100
Range82.4
Interquartile range (IQR)27.4

Descriptive statistics

Standard deviation19.72217292
Coefficient of variation (CV)0.2380804323
Kurtosis0.3574175008
Mean82.83827751
Median Absolute Deviation (MAD)6.5
Skewness-1.217810211
Sum17313.2
Variance388.9641047
MonotocityNot monotonic
2021-03-01T08:14:31.586441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9913
 
5.7%
9711
 
4.8%
9810
 
4.4%
1007
 
3.1%
98.64
 
1.8%
963
 
1.3%
99.63
 
1.3%
92.63
 
1.3%
99.83
 
1.3%
99.73
 
1.3%
Other values (130)149
65.6%
(Missing)18
 
7.9%
ValueCountFrequency (%)
17.61
0.4%
26.61
0.4%
31.41
0.4%
35.91
0.4%
361
0.4%
ValueCountFrequency (%)
1007
3.1%
99.92
 
0.9%
99.83
1.3%
99.73
1.3%
99.63
1.3%

Phones (per 1000)
Real number (ℝ≥0)

MISSING

Distinct214
Distinct (%)96.0%
Missing4
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean236.061435
Minimum0.2
Maximum1035.6
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:31.914544image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile3.6
Q137.8
median176.2
Q3389.65
95-th percentile675.19
Maximum1035.6
Range1035.4
Interquartile range (IQR)351.85

Descriptive statistics

Standard deviation227.9918286
Coefficient of variation (CV)0.9658156515
Kurtosis0.417440623
Mean236.061435
Median Absolute Deviation (MAD)149.4
Skewness1.016070565
Sum52641.7
Variance51980.27391
MonotocityNot monotonic
2021-03-01T08:14:32.258270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.82
 
0.9%
3.62
 
0.9%
7.92
 
0.9%
2.32
 
0.9%
269.52
 
0.9%
255.62
 
0.9%
42
 
0.9%
8.22
 
0.9%
2.72
 
0.9%
8.11
 
0.4%
Other values (204)204
89.9%
(Missing)4
 
1.8%
ValueCountFrequency (%)
0.21
0.4%
1.31
0.4%
1.91
0.4%
2.32
0.9%
2.61
0.4%
ValueCountFrequency (%)
1035.61
0.4%
8981
0.4%
877.71
0.4%
851.41
0.4%
842.41
0.4%

Arable (%)
Real number (ℝ≥0)

ZEROS

Distinct203
Distinct (%)90.2%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean13.79711111
Minimum0
Maximum62.11
Zeros9
Zeros (%)4.0%
Memory size1.9 KiB
2021-03-01T08:14:32.601999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.084
Q13.22
median10.42
Q320
95-th percentile40.436
Maximum62.11
Range62.11
Interquartile range (IQR)16.78

Descriptive statistics

Standard deviation13.04040213
Coefficient of variation (CV)0.9451545342
Kurtosis1.614714224
Mean13.79711111
Median Absolute Deviation (MAD)7.6
Skewness1.334719211
Sum3104.35
Variance170.0520876
MonotocityNot monotonic
2021-03-01T08:14:32.867602image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09
 
4.0%
203
 
1.3%
102
 
0.9%
2.872
 
0.9%
23.282
 
0.9%
2.222
 
0.9%
1.642
 
0.9%
13.62
 
0.9%
2.672
 
0.9%
16.672
 
0.9%
Other values (193)197
86.8%
ValueCountFrequency (%)
09
4.0%
0.021
 
0.4%
0.041
 
0.4%
0.071
 
0.4%
0.141
 
0.4%
ValueCountFrequency (%)
62.111
0.4%
56.211
0.4%
55.31
0.4%
54.41
0.4%
54.021
0.4%

Crops (%)
Real number (ℝ≥0)

ZEROS

Distinct162
Distinct (%)72.0%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean4.564222222
Minimum0
Maximum50.68
Zeros28
Zeros (%)12.3%
Memory size1.9 KiB
2021-03-01T08:14:33.164457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.19
median1.03
Q34.44
95-th percentile19.794
Maximum50.68
Range50.68
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation8.361469745
Coefficient of variation (CV)1.831959387
Kurtosis12.18563338
Mean4.564222222
Median Absolute Deviation (MAD)1.03
Skewness3.225224261
Sum1026.95
Variance69.91417629
MonotocityNot monotonic
2021-03-01T08:14:33.476936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
028
 
12.3%
0.035
 
2.2%
0.194
 
1.8%
0.44
 
1.8%
0.014
 
1.8%
0.143
 
1.3%
2.963
 
1.3%
0.242
 
0.9%
0.972
 
0.9%
0.052
 
0.9%
Other values (152)168
74.0%
ValueCountFrequency (%)
028
12.3%
0.014
 
1.8%
0.022
 
0.9%
0.035
 
2.2%
0.042
 
0.9%
ValueCountFrequency (%)
50.681
0.4%
48.961
0.4%
45.711
0.4%
43.061
0.4%
38.891
0.4%

Other (%)
Real number (ℝ≥0)

Distinct209
Distinct (%)92.9%
Missing2
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean81.63831111
Minimum33.33
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:33.773793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum33.33
5-th percentile47.794
Q171.65
median85.7
Q395.44
95-th percentile99.796
Maximum100
Range66.67
Interquartile range (IQR)23.79

Descriptive statistics

Standard deviation16.14083477
Coefficient of variation (CV)0.1977115224
Kurtosis0.2159729125
Mean81.63831111
Median Absolute Deviation (MAD)10.82
Skewness-0.956341406
Sum18368.62
Variance260.5265471
MonotocityNot monotonic
2021-03-01T08:14:34.055024image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1008
 
3.5%
753
 
1.3%
702
 
0.9%
95.022
 
0.9%
73.332
 
0.9%
70.442
 
0.9%
96.152
 
0.9%
76.322
 
0.9%
802
 
0.9%
67.221
 
0.4%
Other values (199)199
87.7%
(Missing)2
 
0.9%
ValueCountFrequency (%)
33.331
0.4%
33.911
0.4%
34.821
0.4%
40.811
0.4%
42.181
0.4%
ValueCountFrequency (%)
1008
3.5%
99.981
 
0.4%
99.961
 
0.4%
99.931
 
0.4%
99.811
 
0.4%

Climate
Real number (ℝ≥0)

MISSING

Distinct6
Distinct (%)2.9%
Missing22
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean2.13902439
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:34.336256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum4
Range3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.699396816
Coefficient of variation (CV)0.3269700052
Kurtosis0.09013288944
Mean2.13902439
Median Absolute Deviation (MAD)0
Skewness0.3502417606
Sum438.5
Variance0.4891559063
MonotocityNot monotonic
2021-03-01T08:14:34.523737image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2111
48.9%
348
21.1%
129
 
12.8%
1.58
 
3.5%
46
 
2.6%
2.53
 
1.3%
(Missing)22
 
9.7%
ValueCountFrequency (%)
129
 
12.8%
1.58
 
3.5%
2111
48.9%
2.53
 
1.3%
348
21.1%
ValueCountFrequency (%)
46
 
2.6%
348
21.1%
2.53
 
1.3%
2111
48.9%
1.58
 
3.5%

Birthrate
Real number (ℝ≥0)

MISSING

Distinct220
Distinct (%)98.2%
Missing3
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean22.11473214
Minimum7.29
Maximum50.73
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:34.804971image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum7.29
5-th percentile9.0455
Q112.6725
median18.79
Q329.82
95-th percentile43.094
Maximum50.73
Range43.44
Interquartile range (IQR)17.1475

Descriptive statistics

Standard deviation11.1767157
Coefficient of variation (CV)0.5053968382
Kurtosis-0.6106540877
Mean22.11473214
Median Absolute Deviation (MAD)7.75
Skewness0.7079609905
Sum4953.7
Variance124.9189739
MonotocityNot monotonic
2021-03-01T08:14:35.086200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.482
 
0.9%
12.562
 
0.9%
18.022
 
0.9%
18.792
 
0.9%
37.011
 
0.4%
22.81
 
0.4%
33.911
 
0.4%
17.591
 
0.4%
10.271
 
0.4%
13.521
 
0.4%
Other values (210)210
92.5%
(Missing)3
 
1.3%
ValueCountFrequency (%)
7.291
0.4%
8.251
0.4%
8.481
0.4%
8.711
0.4%
8.721
0.4%
ValueCountFrequency (%)
50.731
0.4%
49.821
0.4%
47.351
0.4%
46.61
0.4%
45.761
0.4%

Deathrate
Real number (ℝ≥0)

MISSING

Distinct201
Distinct (%)90.1%
Missing4
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean9.241345291
Minimum2.29
Maximum29.74
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:35.367429image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum2.29
5-th percentile4.147
Q15.91
median7.84
Q310.605
95-th percentile19.87
Maximum29.74
Range27.45
Interquartile range (IQR)4.695

Descriptive statistics

Standard deviation4.990026177
Coefficient of variation (CV)0.5399675068
Kurtosis3.186695962
Mean9.241345291
Median Absolute Deviation (MAD)2.29
Skewness1.652541635
Sum2060.82
Variance24.90036125
MonotocityNot monotonic
2021-03-01T08:14:35.633039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.372
 
0.9%
6.292
 
0.9%
5.582
 
0.9%
3.922
 
0.9%
5.282
 
0.9%
9.832
 
0.9%
9.052
 
0.9%
6.522
 
0.9%
7.842
 
0.9%
8.262
 
0.9%
Other values (191)203
89.4%
(Missing)4
 
1.8%
ValueCountFrequency (%)
2.291
0.4%
2.411
0.4%
2.581
0.4%
2.651
0.4%
3.271
0.4%
ValueCountFrequency (%)
29.741
0.4%
29.51
0.4%
28.711
0.4%
24.21
0.4%
23.11
0.4%

Agriculture
Real number (ℝ≥0)

MISSING

Distinct150
Distinct (%)70.8%
Missing15
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean0.1508443396
Minimum0
Maximum0.769
Zeros1
Zeros (%)0.4%
Memory size1.9 KiB
2021-03-01T08:14:35.945516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.03775
median0.099
Q30.221
95-th percentile0.4489
Maximum0.769
Range0.769
Interquartile range (IQR)0.18325

Descriptive statistics

Standard deviation0.1467979535
Coefficient of variation (CV)0.9731750883
Kurtosis1.884295315
Mean0.1508443396
Median Absolute Deviation (MAD)0.071
Skewness1.418650218
Sum31.979
Variance0.02154963916
MonotocityNot monotonic
2021-03-01T08:14:36.242386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.018
 
3.5%
0.046
 
2.6%
0.035
 
2.2%
0.064
 
1.8%
0.0184
 
1.8%
0.053
 
1.3%
0.073
 
1.3%
0.0543
 
1.3%
0.0333
 
1.3%
0.0383
 
1.3%
Other values (140)170
74.9%
(Missing)15
 
6.6%
ValueCountFrequency (%)
01
0.4%
0.0012
0.9%
0.0021
0.4%
0.0042
0.9%
0.0052
0.9%
ValueCountFrequency (%)
0.7691
0.4%
0.651
0.4%
0.621
0.4%
0.5641
0.4%
0.552
0.9%

Industry
Real number (ℝ≥0)

MISSING

Distinct155
Distinct (%)73.5%
Missing16
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean0.2827109005
Minimum0.02
Maximum0.906
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:36.554851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.0975
Q10.193
median0.272
Q30.341
95-th percentile0.5655
Maximum0.906
Range0.886
Interquartile range (IQR)0.148

Descriptive statistics

Standard deviation0.1382722188
Coefficient of variation (CV)0.4890940484
Kurtosis2.336708083
Mean0.2827109005
Median Absolute Deviation (MAD)0.074
Skewness1.099606632
Sum59.652
Variance0.0191192065
MonotocityNot monotonic
2021-03-01T08:14:36.820472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.114
 
1.8%
0.174
 
1.8%
0.3123
 
1.3%
0.23
 
1.3%
0.2593
 
1.3%
0.183
 
1.3%
0.1883
 
1.3%
0.2943
 
1.3%
0.13
 
1.3%
0.193
 
1.3%
Other values (145)179
78.9%
(Missing)16
 
7.0%
ValueCountFrequency (%)
0.021
0.4%
0.0321
0.4%
0.041
0.4%
0.0541
0.4%
0.0621
0.4%
ValueCountFrequency (%)
0.9061
0.4%
0.8011
0.4%
0.6661
0.4%
0.6581
0.4%
0.6131
0.4%

Service
Real number (ℝ≥0)

MISSING

Distinct167
Distinct (%)78.8%
Missing15
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean0.5652830189
Minimum0.062
Maximum0.954
Zeros0
Zeros (%)0.0%
Memory size1.9 KiB
2021-03-01T08:14:37.132951image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.062
5-th percentile0.26375
Q10.42925
median0.571
Q30.6785
95-th percentile0.8345
Maximum0.954
Range0.892
Interquartile range (IQR)0.24925

Descriptive statistics

Standard deviation0.1658409993
Coefficient of variation (CV)0.2933769347
Kurtosis-0.1869121633
Mean0.5652830189
Median Absolute Deviation (MAD)0.113
Skewness-0.1369389891
Sum119.84
Variance0.02750323706
MonotocityNot monotonic
2021-03-01T08:14:37.382918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6845
 
2.2%
0.554
 
1.8%
0.624
 
1.8%
0.5793
 
1.3%
0.5493
 
1.3%
0.6033
 
1.3%
0.463
 
1.3%
0.6652
 
0.9%
0.6432
 
0.9%
0.4032
 
0.9%
Other values (157)181
79.7%
(Missing)15
 
6.6%
ValueCountFrequency (%)
0.0621
0.4%
0.1771
0.4%
0.1971
0.4%
0.211
0.4%
0.2441
0.4%
ValueCountFrequency (%)
0.9541
0.4%
0.931
0.4%
0.9271
0.4%
0.921
0.4%
0.9061
0.4%

Interactions

2021-03-01T08:12:55.009375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:55.368744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:55.899938image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:56.259289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:56.571772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:56.977991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:57.288374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:57.561647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:57.827269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:58.108499image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:58.374090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:58.655319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:58.936550image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:59.186531image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:59.436521image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:12:59.795865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:00.170838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:00.498942image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:00.795796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:01.139525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:01.420753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:01.780104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:02.092586image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:02.358186image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:02.670665image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:02.967525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:03.280004image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:03.608103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:03.873707image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:04.233058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:04.623655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:05.128526image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:05.441007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:05.769105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:06.065960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:06.331566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:06.659670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:06.989062image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:07.332784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:07.645267image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:07.942117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:08.238973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:08.535826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:08.817061image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:09.129540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:09.426390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:09.723247image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:10.004480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:10.316952image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:10.645060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:10.926289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:11.238764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:11.535618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:11.848096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:12.144957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:12.363691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:12.644918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:12.894901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:13.160505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:13.441736image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:13.707342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:13.957330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:14.222934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:14.504164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:14.769769image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:15.035382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:15.347853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:15.769700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:16.004058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:16.347785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:16.644639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:16.957119image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:17.207101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:17.488330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:17.785185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:18.003923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:18.269530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:18.597629image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:18.863236image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:19.144465image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:19.441321image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:19.722551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:20.035030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:20.378755image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:20.644363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:20.878721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:21.206824image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:21.519305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:21.847404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:22.144263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:22.487984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:22.862957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:23.175436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:23.503540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:23.831644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:24.144120image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:24.519093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:24.862818image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:25.190922image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:25.472153image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:25.800259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:26.097108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:26.362714image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:26.690819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:27.034543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:27.378271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:27.628256image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:27.893861image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:28.221965image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:28.471947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:28.753174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:29.268764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:29.534374image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:29.815604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:30.143705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:30.424937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:30.737411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:31.049894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:31.331123image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:31.565485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:31.799838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:32.096693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:32.346678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:32.565416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:32.846641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:33.143501image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:33.377856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:33.580966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:33.815329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:34.034060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:34.315295image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:34.534030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:34.752778image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:34.971495image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:35.237101image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:35.440216image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:35.690196image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:36.033921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:36.315155image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:36.580762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:36.893234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:37.205713image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:37.486944image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:37.705684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:37.940042image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:38.174405image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:38.393137image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:38.658740image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:38.893102image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:39.127459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:39.377440image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:39.627423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:39.846160image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:40.049269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:40.314875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:40.611728image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:40.892963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:41.158568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:41.392924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:41.721028image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:42.033506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:42.283491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:42.533473image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:42.783454image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:43.080306image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:43.314673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:43.799007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:44.064619image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:44.361472image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:44.689569image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:44.970801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:45.220788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:45.470771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:45.720768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:45.955127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:46.189471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:46.439455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:46.689431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:46.923809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:47.173777image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:47.392509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:47.642496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:47.876854image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:48.126838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:48.345572image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:48.611174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:48.829909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:49.095517image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:49.392373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:49.689228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:49.986078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:50.251685image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:50.564163image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:50.861019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:51.111007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:51.361002image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:51.642219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:51.907820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:52.173431image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:52.439054image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:52.673396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:52.954623image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:53.282725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:53.579579image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:53.860808image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:54.157666image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:54.423271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:54.704507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:54.938863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:55.173220image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:55.454468image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:55.735684image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:55.954435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:56.188780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:56.454381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:56.688744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:56.969973image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:57.219954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:57.454314image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:57.719916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:57.954279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:58.188636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:58.422998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:58.672977image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:58.922960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:59.157336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:59.376057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:59.626055image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:13:59.844771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:00.094774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:00.313487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:00.563471image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:00.782211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:01.313417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:01.579043image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:01.829013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:02.094620image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:02.313351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:02.516464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:02.750825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:03.016427image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:03.282036image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:03.500772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:03.719504image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:03.985112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:04.203846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:04.438223image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:04.672567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:04.906924image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:05.141299image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:05.391266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:05.641248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:05.859981image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:06.172459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:06.406835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:06.625555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:06.906785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:07.203636image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:07.531742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:07.828597image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:08.094204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:08.391059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:08.687928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:08.969157image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:09.234745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:09.500354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:09.781585image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:10.062813image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:10.390913image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:10.687774image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:10.984622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:11.250249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:11.515840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:11.781461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:12.031428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:12.281428image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:12.500143image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:12.718878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:13.000110image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:13.250091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:13.468831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:13.718810image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:13.984417image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:14.265645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:14.515633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:14.765612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:15.015594image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:15.296823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:15.640551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:15.937404image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:16.187408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:16.452995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:16.734224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:17.015456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:17.265437image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:17.562293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:17.796650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:18.062260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:18.280993image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:18.546599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:18.812205image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:19.093435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:19.390290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:19.640272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:19.874634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-01T08:14:20.171486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-01T08:14:37.664149image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-01T08:14:38.304744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-01T08:14:38.945307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-01T08:14:39.679634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-03-01T08:14:20.746176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-01T08:14:22.027342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-03-01T08:14:22.808539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-03-01T08:14:23.980330image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

CountryRegionPopulationArea (sq. mi.)Pop. Density (per sq. mi.)Coastline (coast/area ratio)Net migrationInfant mortality (per 1000 births)GDP ($ per capita)Literacy (%)Phones (per 1000)Arable (%)Crops (%)Other (%)ClimateBirthrateDeathrateAgricultureIndustryService
0AfghanistanASIA (EX. NEAR EAST)3105699764750048.00.0023.06163.07700.036.03.212.130.2287.651.046.6020.340.3800.2400.380
1AlbaniaEASTERN EUROPE358165528748124.61.26-4.9321.524500.086.571.221.094.4274.493.015.115.220.2320.1880.579
2AlgeriaNORTHERN AFRICA32930091238174013.80.04-0.3931.006000.070.078.13.220.2596.531.017.144.610.1010.6000.298
3American SamoaOCEANIA57794199290.458.29-20.719.278000.097.0259.510.0015.0075.002.022.463.27NaNNaNNaN
4AndorraWESTERN EUROPE71201468152.10.006.604.0519000.0100.0497.22.220.0097.783.08.716.25NaNNaNNaN
5AngolaSUB-SAHARAN AFRICA1212707112467009.70.130.00191.191900.042.07.82.410.2497.35NaN45.1124.200.0960.6580.246
6AnguillaLATIN AMER. & CARIB13477102132.159.8010.7621.038600.095.0460.00.000.00100.002.014.175.340.0400.1800.780
7Antigua & BarbudaLATIN AMER. & CARIB69108443156.034.54-6.1519.4611000.089.0549.918.184.5577.272.016.935.370.0380.2200.743
8ArgentinaLATIN AMER. & CARIB39921833276689014.40.180.6115.1811200.097.1220.412.310.4887.213.016.737.550.0950.3580.547
9ArmeniaC.W. OF IND. STATES29763722980099.90.00-6.4723.283500.098.6195.717.552.3080.154.012.078.230.2390.3430.418

Last rows

CountryRegionPopulationArea (sq. mi.)Pop. Density (per sq. mi.)Coastline (coast/area ratio)Net migrationInfant mortality (per 1000 births)GDP ($ per capita)Literacy (%)Phones (per 1000)Arable (%)Crops (%)Other (%)ClimateBirthrateDeathrateAgricultureIndustryService
217VanuatuOCEANIA2088691220017.120.720.0055.162900.053.032.62.467.3890.162.022.727.820.2600.1200.620
218VenezuelaLATIN AMER. & CARIB2573043591205028.20.31-0.0422.204800.093.4140.12.950.9296.132.018.714.920.0400.4190.541
219VietnamASIA (EX. NEAR EAST)84402966329560256.11.05-0.4525.952500.090.3187.719.975.9574.082.016.866.220.2090.4100.381
220Virgin IslandsLATIN AMER. & CARIB108605191056.99.84-8.948.0317200.0NaN652.811.762.9485.302.013.966.430.0100.1900.800
221Wallis and FutunaOCEANIA1602527458.547.08NaNNaN3700.050.0118.65.0025.0070.002.0NaNNaNNaNNaNNaN
222West BankNEAR EAST24604925860419.90.002.9819.62800.0NaN145.216.9018.9764.133.031.673.920.0900.2800.630
223Western SaharaNORTHERN AFRICA2730082660001.00.42NaNNaNNaNNaNNaN0.020.0099.981.0NaNNaNNaNNaN0.400
224YemenNEAR EAST2145618852797040.60.360.0061.50800.050.237.22.780.2496.981.042.898.300.1350.4720.393
225ZambiaSUB-SAHARAN AFRICA1150201075261415.30.000.0088.29800.080.68.27.080.0392.902.041.0019.930.2200.2900.489
226ZimbabweSUB-SAHARAN AFRICA1223680539058031.30.000.0067.691900.090.726.88.320.3491.342.028.0121.840.1790.2430.579